Vetted Data Validation Professionals

“LLM/agentic systems practitioner who partners directly with customers to productionize prototypes end-to-end—defining business-aligned metrics, building evaluation datasets, and shipping monitored, cost-bounded inference APIs on AWS Lambda. Notably delivered a vehicle damage classification system that cut manual review by 40% and stabilized agent workflows by instrumenting state transitions to uncover and fix a race-condition-driven skipped tool call.”

“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”

“Early-career full-stack engineer with internship experience at Bloomberg and Capital One, shipping both data-heavy product features and AI-powered internal tools. Stands out for owning end-to-end user-facing systems, from spreadsheet-to-dashboard automation that cut workflows from hours to seconds to a BERT-based support chatbot that improved reliability by ~25%.”

Sri Yogesh Dorbala

Screened

Engineering Manager / Tech Lead specializing in large-scale distributed systems

Seattle, WA8y exp

“Software engineer focused on personalization and data/ML infrastructure who built a GenAI/LLM-driven carousel ranking system end-to-end, delivering a reported 6–7% order-rate lift. Also designed large-scale personalization ETL (15PB for ~100M users) and created a custom Airflow operator to integrate with Databricks under enterprise version constraints, with hands-on on-call and data-quality reliability improvements.”

“Software engineer with recent hands-on experience building a Google internal startup-style AML/KYC compliance platform for financial services clients. They combine full-stack product work with backend/data systems expertise, delivering measurable impact including 44% higher screening throughput, 94% fewer false positives, and 33% faster client onboarding.”

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Built and shipped an end-to-end LLM agent during an AT&T internship to automate network troubleshooting, with production-style reliability safeguards (timeouts/retries/fallbacks) and structured, state-machine orchestration; project won 3rd place in AT&T’s nationwide intern innovation challenge and was demoed to leadership. Also handled messy multi-partner data at Tencent by implementing schema validation/normalization, confidence-threshold fallbacks, and idempotent Python/ORM-based pipelines.”

“Meta engineer who improved a critical revenue estimation dataset pipeline that was arriving ~6 days late—diagnosed via raw logs/lineage, redesigned legacy scans to only process the needed window, and shipped validation plus freshness/lag dashboards. Delivered ~50% latency reduction (to ~3 days) and regained adoption by running old/new pipelines in parallel with gated cutover and evidence-based customer communication. Applies incident-response rigor to real-time LLM/agentic workflow debugging and regularly runs developer demos/workshops.”

Ahmed Sadaqat

Screened

Senior Machine Learning Engineer specializing in production ML and predictive analytics

Los Angeles, CA7y exp

“ML/AI engineering leader who has owned end-to-end production systems from experimentation through deployment, monitoring, and iteration at meaningful scale. They describe running a 1M+ records/day prediction platform with 99.9% availability, shipping a RAG-based conversational AI feature for 50,000 active users, and consistently improving precision, latency, reliability, and cost with measurable business impact.”

“Fintech and AI product engineer who has owned major production rollouts, including Lending Club's banking-arm launch, and has since built LLM-powered decision systems for finance and climate use cases. Particularly strong in combining stakeholder management with pragmatic architecture choices like observability, deterministic pipeline design, RAG, and document-to-structured-data workflows.”

“Frontend engineer from Amazon Advertising who owned a sophisticated React/TypeScript ad creative builder used by advertisers and ad ops teams. Stands out for combining deep browser-level debugging with product-minded UX improvements that reduced support escalations and made complex multi-placement ad configuration faster and more reliable for power users.”

“Data engineer focused on building and operating reliable Airflow-orchestrated pipelines into BigQuery, including daily billing ingestion (~1GB/day) and ad platform (Facebook/LinkedIn) data collection. Implemented end-to-end data quality checks plus org-wide incident response automation integrating PagerDuty, Slack, and Jira, and has experience executing large backfills (4–5TB) via time-window batching.”

Pratham Thukral

Screened

Mid-level Software Engineer specializing in distributed systems on AWS

Seattle, WA3y exp

“Data/infra engineer with AWS DynamoDB experience who has shipped reliability-critical systems (Global Tables replica repair protocol) and customer-facing service rollouts using canary/percentage-based deployments, strong observability, and rollback strategies. Also built end-to-end Airflow pipelines producing weekly automated reports over ~10TB of advertising segment data, with rigorous week-over-week data quality validation.”

“Early-stage full-stack engineer who built Dokai's core web spreadsheet product and key AI features with just a two-engineer team. They combine strong product ownership with practical LLM integration experience, including reducing onboarding from a week to five minutes and solving difficult reliability and memory issues in production.”